AIML Enginner

About the Role

We are seeking an experienced AI/ML Engineer with 25 years of hands-on experience in building and deploying machine learning solutions in the fintech and spend management domain. You will work on real-time forecasting, intelligent document processing (invoices/receipts), fraud detection, and other AI-powered features that enhance our finance intelligence platform. This role demands expertise in both time series forecasting and computer vision, as well as a solid understanding of how ML applies to enterprise finance operations.

Key Responsibilities

• Design, train, and deploy ML models for spend forecasting, budget prediction, expense categorization, and risk scoring.
• Build and optimize OCR-based invoice and receipt parsing systems using computer vision and NLP techniques.
• Implement time-series models (Prophet, ARIMA, LSTM, XGBoost, etc.) for forecasting trends in financial transactions, expenses, and vendor payments.
• Work on intelligent document classification, key-value extraction, and line-item detection from unstructured financial documents (PDFs, scanned images).
• Collaborate with product and finance teams to define high-impact AI use cases and deliver business-ready solutions.
• Integrate ML pipelines into production using scalable tools and platforms (Docker, CI/CD, cloud services).
• Monitor model performance post-deployment, conduct drift analysis, and implement retraining strategies.

Required Skills & Qualifications

Core Machine Learning

• Strong knowledge of supervised and unsupervised ML techniques applied to structured and semi-structured financial data.

• Experience in time-series analysis and forecasting algorithms such as:

• ARIMA, SARIMA

• Facebook Prophet

• XGBoost for regression

• LSTM / GRU models for sequential data

• Proficiency in Python and key libraries: scikit-learn, Pandas, NumPy, StatsModels, PyTorch, TensorFlow.

Computer Vision & Document AI

• Hands-on experience with OCR tools such as Tesseract, Google Vision API, or AWS Textract.
• Knowledge of document layout analysis and field-level extraction using OpenCV, LayoutLM, or Google Document AI.
• Familiarity with annotation tools (Label Studio, CVAT) and post-processing OCR outputs for structured data extraction.

Deployment & Engineering

• Experience in exposing ML models via Flask or FastAPI.
• Model packaging and deployment with Docker, version control with Git, and ML lifecycle tools like MLflow or DVC.
• Working knowledge of cloud platforms (AWS/GCP/Azure) and integrating models with backend microservices.

Data & Domain

• Understanding of financial documents: invoices, receipts, expense reports, and GL data.
• Ability to work with tabular, image-based, and PDF-based financial datasets.
• SQL proficiency and familiarity with financial databases or ERP systems is a plus.

Preferred Qualifications

• Experience with fraud detection, anomaly detection, or compliance risk models in the fintech space.
• Familiarity with vector databases (like Milvus, Pinecone) and embedding models (e.g., OpenAI, BERT, Sentence-BERT) for semantic search in finance.